How much does an image labeling company cost?
Pricing depends on annotation type (boxes vs pixel masks), ontology size, edge-case rate, and required QA depth. Abaka can price work using published reference rates such as Image Editing at $8/hr and Dense Captioning at $6/hr, then scope a pilot to estimate per-image costs for your dataset. For some vision programs we also use per-unit pricing where applicable, but we won’t invent a per-label rate without a pilot. Talk to an Expert with 100–300 samples to get a concrete quote and timeline.
How fast can you deliver labeled images once we start?
Most teams see meaningful deliveries in 2–3 weeks because the first phase is about getting the spec right: ontology, guidelines, tooling setup, and QA gates. After a pilot batch, we ramp into weekly drops with a predictable cadence. Throughput depends on complexity and your acceptance tests; we plan capacity with practical limits (for example, 500 files/day per annotator maximum throughput where applicable) to avoid quality collapse during ramp. If you have a hard deadline, we’ll design a phased delivery plan.
What image annotation formats do you support (COCO, YOLO, masks)?
We support common computer vision deliverables including COCO JSON (boxes, segmentation polygons/RLE, keypoints), YOLO TXT, PNG mask exports, and vendor-neutral JSON/CSV sidecars for attributes and metadata. If your pipeline uses a custom schema, we can map ontologies and produce a validated export package with manifests and consistent naming. Abaka Forge tracks dataset versions and guideline changes so your team can reproduce exactly what was used for each training run.
How do you ensure annotation accuracy and consistency?
Accuracy comes from process: calibrated annotators, gold tasks, rubric-based reviews, and adjudication for ambiguous cases. We define acceptance tests up front and track error categories by class so you can see what’s improving and what needs tighter rules. Abaka programs can target 99% accuracy for priority classes by adding reviewer depth where it matters most. Abaka Forge logs edits and reviewer decisions, enabling traceability and faster root-cause analysis when model evaluation flags a data issue.
Is Abaka secure enough for sensitive images and internal datasets?
Yes—Abaka operates with SOC 2 and ISO 27001 aligned practices, GDPR/CCPA alignment, strict NDAs, and segregated secure pipelines. Access can be restricted by project and role, and we maintain audit-friendly records of who worked on what and when. We also emphasize provenance: your data is exclusively yours and is never repurposed, resold, or shared. If you need additional controls (custom access rules, dedicated environments), we’ll scope them during Day 0–3 onboarding.
Can you label multilingual image datasets (labels, attributes, metadata)?
Yes. While image geometry is language-agnostic, attributes, metadata, and captions often require multilingual coverage. Abaka operates across 50+ countries and can staff multilingual teams for taxonomy terms, product attributes, signage text context, and localized guidelines. We also normalize label vocabularies so your downstream pipeline uses consistent class IDs even when reviewer language differs. If you need bilingual exports (e.g., English + local language), we can deliver parallel fields in JSON/CSV with controlled vocabularies.
How is Abaka different from other image labeling vendors?
Abaka is built for frontier AI programs: secure, auditable, and quality-driven. We don’t just “staff a queue”—we establish versioned guidelines, measurable QA gates, and delivery packaging that matches training pipelines. Abaka Forge supports consistent workflows and faster iteration, and our governance posture includes SOC 2 and ISO 27001 aligned operations plus strict NDAs. A key trust differentiator: we never build models that compete with you, and your dataset is never repurposed or resold.
What if we need to change the ontology or instructions mid-project?
Change requests are normal—new classes, new attributes, new edge cases. We handle this through controlled versioning: document the update, run a small calibration batch, and then apply changes to new production work. If prior data must be updated, we scope targeted relabeling so you only pay to fix impacted slices rather than relabeling everything. Abaka Forge keeps history of guideline versions and dataset exports, which helps your team compare training runs and avoid silent spec drift.
Can we start with a small pilot before committing to a large labeling program?
Yes—starting with a pilot is recommended. We typically begin with 100–300 representative images (or a small set of videos/frames) to validate the ontology, confirm edge-case rules, and estimate throughput and cost. The pilot also produces a concrete error taxonomy and acceptance workflow so your reviewers know exactly what to check. After you approve pilot outputs, we ramp capacity to meet your weekly delivery goals without sacrificing consistency.
Who owns the labeled data and can you reuse it?
You own your data and the labeled outputs. Abaka’s policy is that your data is exclusively yours—never repurposed, resold, or shared. We also maintain full IP provenance for collected data programs, supporting 0% copyright risk on collected assets. If you provide the raw images, we treat them as your confidential materials under strict NDAs and segregated pipelines. If you require explicit contractual language around ownership and retention, we support that during procurement.
What tools do you use for image labeling and review?
We use Abaka Forge—our platform for collection, cleaning, annotation, QA, and production delivery. For image work, Forge supports boxes, polygons, segmentation masks, keypoints, and attribute labeling with workflow controls (queues, rework, reviewer stages) and audit logs. The platform can accelerate throughput with large-model automation where appropriate, while still keeping human reviewers accountable for acceptance. If your team needs specific exports, we configure validation checks so deliveries match your pipeline.
What is the minimum dataset size you can handle?
There’s no hard minimum. We support everything from small research sets (a few hundred images) to large production programs (hundreds of thousands or more). What matters is clarity: a stable ontology, representative samples, and defined acceptance tests. For very small jobs, we’ll recommend a tight pilot-style workflow to avoid overhead; for larger jobs, we’ll implement capacity planning and QA gates to keep consistency as volume grows. Talk to an Expert and we’ll suggest the right engagement model.